OpenRaiser/Intern-Atlas huggingface dataset:
Generative Motion Stylization of Cross-structure Characters within Canonical Motion Space
Venue: arXiv cs.CV 2024
Year: 2024

Stylized motion breathes life into characters. However, the fixed skeleton structure and style representation hinder existing data-driven motion synthesis methods from generating stylized motion for various characters. In this work, we propose a generative motion stylization pipeline, named MotionS, for synthesizing diverse and stylized motion on cross-structure characters using cross-modality style prompts. Our key insight is to embed motion style into a cross-modality latent space and perceive the cross-structure skeleton topologies, allowing for motion stylization within a canonical motion space. Specifically, the large-scale Contrastive-Language-Image-Pre-training (CLIP) model is leveraged to construct the cross-modality latent space, enabling flexible style representation within it. Additionally, two topology-encoded tokens are learned to capture the canonical and specific skeleton topologies, facilitating cross-structure topology shifting. Subsequently, the topology-shifted stylization diffusion is designed to generate motion content for the particular skeleton and stylize it in the shifted canonical motion space using multi-modality style descriptions. Through an extensive set of examples, we demonstrate the flexibility and generalizability of our pipeline across various characters and style descriptions. Qualitative and quantitative comparisons show the superiority of our pipeline over state-of-the-arts, consistently delivering high-quality stylized motion across a broad spectrum of skeletal structures.
Hierarchical Spatial Proximity Reasoning for Vision-and-Language Navigation
Venue: arXiv cs.CV 2024
Year: 2024

Most Vision-and-Language Navigation (VLN) algorithms are prone to making inaccurate decisions due to their lack of visual common sense and limited reasoning capabilities. To address this issue, we propose a Hierarchical Spatial Proximity Reasoning (HSPR) method. First, we introduce a scene understanding auxiliary task to help the agent build a knowledge base of hierarchical spatial proximity. This task utilizes panoramic views and object features to identify types of nodes and uncover the adjacency relationships between nodes, objects, and between nodes and objects. Second, we propose a multi-step reasoning navigation algorithm based on the hierarchical spatial proximity knowledge base, which continuously plans feasible paths to enhance exploration efficiency. Third, we introduce a residual fusion method to improve navigation decision accuracy. Finally, we validate our approach with experiments on publicly available datasets including REVERIE, SOON, R2R, and R4R. Our code is available at https://github.com/iCityLab/HSPR
OurDB: Ouroboric Domain Bridging for Multi-Target Domain Adaptive Semantic Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Multi-target domain adaptation (MTDA) for semantic segmentation poses a significant challenge, as it involves multiple target domains with varying distributions. The goal of MTDA is to minimize the domain discrepancies among a single source and multi-target domains, aiming to train a single model that excels across all target domains. Previous MTDA approaches typically employ multiple teacher architectures, where each teacher specializes in one target domain to simplify the task. However, these architectures hinder the student model from fully assimilating comprehensive knowledge from all target-specific teachers and escalate training costs with increasing target domains. In this paper, we propose an ouroboric domain bridging (OurDB) framework, offering an efficient solution to the MTDA problem using a single teacher architecture. This framework dynamically cycles through multiple target domains, aligning each domain individually to restrain the biased alignment problem, and utilizes Fisher information to minimize the forgetting of knowledge from previous target domains. We also propose a context-guided class-wise mixup (CGMix) that leverages contextual information tailored to diverse target contexts in MTDA. Experimental evaluations conducted on four urban driving datasets (i.e., GTA5, Cityscapes, IDD, and Mapillary) demonstrate the superiority of our method over existing state-of-the-art approaches.
CRS-Diff: Controllable Remote Sensing Image Generation with Diffusion Model
Venue: arXiv cs.CV 2024
Year: 2024

The emergence of generative models has revolutionized the field of remote sensing (RS) image generation. Despite generating high-quality images, existing methods are limited in relying mainly on text control conditions, and thus do not always generate images accurately and stably. In this paper, we propose CRS-Diff, a new RS generative framework specifically tailored for RS image generation, leveraging the inherent advantages of diffusion models while integrating more advanced control mechanisms. Specifically, CRS-Diff can simultaneously support text-condition, metadata-condition, and image-condition control inputs, thus enabling more precise control to refine the generation process. To effectively integrate multiple condition control information, we introduce a new conditional control mechanism to achieve multi-scale feature fusion, thus enhancing the guiding effect of control conditions. To our knowledge, CRS-Diff is the first multiple-condition controllable RS generative model. Experimental results in single-condition and multiple-condition cases have demonstrated the superior ability of our CRS-Diff to generate RS images both quantitatively and qualitatively compared with previous methods. Additionally, our CRS-Diff can serve as a data engine that generates high-quality training data for downstream tasks, e.g., road extraction. The code is available at https://github.com/Sonettoo/CRS-Diff.
LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models
Venue: arXiv cs.CV 2024
Year: 2024

Customization generation techniques have significantly advanced the synthesis of specific concepts across varied contexts. Multi-concept customization emerges as the challenging task within this domain. Existing approaches often rely on training a fusion matrix of multiple Low-Rank Adaptations (LoRAs) to merge various concepts into a single image. However, we identify this straightforward method faces two major challenges: 1) concept confusion, where the model struggles to preserve distinct individual characteristics, and 2) concept vanishing, where the model fails to generate the intended subjects. To address these issues, we introduce LoRA-Composer, a training-free framework designed for seamlessly integrating multiple LoRAs, thereby enhancing the harmony among different concepts within generated images. LoRA-Composer addresses concept vanishing through concept injection constraints, enhancing concept visibility via an expanded cross-attention mechanism. To combat concept confusion, concept isolation constraints are introduced, refining the self-attention computation. Furthermore, latent re-initialization is proposed to effectively stimulate concept-specific latent within designated regions. Our extensive testing showcases a notable enhancement in LoRA-Composer's performance compared to standard baselines, especially when eliminating the image-based conditions like canny edge or pose estimations. Code is released at \url{https://github.com/Young98CN/LoRA_Composer}
Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring
Venue: arXiv cs.AI 2024
Year: 2024

Counterfactual explanations suggest what should be different in the input instance to change the outcome of an AI system. When dealing with counterfactual explanations in the field of Predictive Process Monitoring, however, control flow relationships among events have to be carefully considered. A counterfactual, indeed, should not violate control flow relationships among activities (temporal background knowledege). Within the field of Explainability in Predictive Process Monitoring, there have been a series of works regarding counterfactual explanations for outcome-based predictions. However, none of them consider the inclusion of temporal background knowledge when generating these counterfactuals. In this work, we adapt state-of-the-art techniques for counterfactual generation in the domain of XAI that are based on genetic algorithms to consider a series of temporal constraints at runtime. We assume that this temporal background knowledge is given, and we adapt the fitness function, as well as the crossover and mutation operators, to maintain the satisfaction of the constraints. The proposed methods are evaluated with respect to state-of-the-art genetic algorithms for counterfactual generation and the results are presented. We showcase that the inclusion of temporal background knowledge allows the generation of counterfactuals more conformant to the temporal background knowledge, without however losing in terms of the counterfactual traditional quality metrics.
Diffusion-Based Environment-Aware Trajectory Prediction
Venue: arXiv cs.CV 2024
Year: 2024

The ability to predict the future trajectories of traffic participants is crucial for the safe and efficient operation of autonomous vehicles. In this paper, a diffusion-based generative model for multi-agent trajectory prediction is proposed. The model is capable of capturing the complex interactions between traffic participants and the environment, accurately learning the multimodal nature of the data. The effectiveness of the approach is assessed on large-scale datasets of real-world traffic scenarios, showing that our model outperforms several well-established methods in terms of prediction accuracy. By the incorporation of differential motion constraints on the model output, we illustrate that our model is capable of generating a diverse set of realistic future trajectories. Through the use of an interaction-aware guidance signal, we further demonstrate that the model can be adapted to predict the behavior of less cooperative agents, emphasizing its practical applicability under uncertain traffic conditions.
TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models
Venue: arXiv cs.CV 2024
Year: 2024

Test-Time Training (TTT) proposes to adapt a pre-trained network to changing data distributions on-the-fly. In this work, we propose the first TTT method for 3D semantic segmentation, TTT-KD, which models Knowledge Distillation (KD) from foundation models (e.g. DINOv2) as a self-supervised objective for adaptation to distribution shifts at test-time. Given access to paired image-pointcloud (2D-3D) data, we first optimize a 3D segmentation backbone for the main task of semantic segmentation using the pointclouds and the task of 2D $\to$ 3D KD by using an off-the-shelf 2D pre-trained foundation model. At test-time, our TTT-KD updates the 3D segmentation backbone for each test sample, by using the self-supervised task of knowledge distillation, before performing the final prediction. Extensive evaluations on multiple indoor and outdoor 3D segmentation benchmarks show the utility of TTT-KD, as it improves performance for both in-distribution (ID) and out-of-distribution (ODO) test datasets. We achieve a gain of up to 13% mIoU (7% on average) when the train and test distributions are similar and up to 45% (20% on average) when adapting to OOD test samples.
Hardware Design and Learning-Based Software Architecture of Musculoskeletal Wheeled Robot Musashi-W for Real-World Applications
Venue: arXiv cs.RO 2024
Year: 2024

Various musculoskeletal humanoids have been developed so far. While these humanoids have the advantage of their flexible and redundant bodies that mimic the human body, they are still far from being applied to real-world tasks. One of the reasons for this is the difficulty of bipedal walking in a flexible body. Thus, we developed a musculoskeletal wheeled robot, Musashi-W, by combining a wheeled base and musculoskeletal upper limbs for real-world applications. Also, we constructed its software system by combining static and dynamic body schema learning, reflex control, and visual recognition. We show that the hardware and software of Musashi-W can make the most of the advantages of the musculoskeletal upper limbs, through several tasks of cleaning by human teaching, carrying a heavy object considering muscle addition, and setting a table through dynamic cloth manipulation with variable stiffness.
Just Undo It: Exploring Undo Mechanics in Multi-User Virtual Reality
Venue: arXiv cs.HC 2024
Year: 2024

With the proliferation of VR and a metaverse on the horizon, many multi-user activities are migrating to the VR world, calling for effective collaboration support. As one key feature, traditional collaborative systems provide users with undo mechanics to reverse errors and other unwanted changes. While undo has been extensively researched in this domain and is now considered industry standard, it is strikingly absent for VR systems in research and industry. This work addresses this research gap by exploring different undo techniques for basic object manipulation in different collaboration modes in VR. We conducted a study involving 32 participants organized in teams of two. Here, we studied users' performance and preferences in a tower stacking task, varying the available undo techniques and their mode of collaboration. The results suggest that users desire and use undo in VR and that the choice of the undo technique impacts users' performance and social connection.
Efficient Feature Extraction and Late Fusion Strategy for Audiovisual Emotional Mimicry Intensity Estimation
Venue: arXiv cs.MM 2024
Year: 2024

In this paper, we present the solution to the Emotional Mimicry Intensity (EMI) Estimation challenge, which is part of 6th Affective Behavior Analysis in-the-wild (ABAW) Competition.The EMI Estimation challenge task aims to evaluate the emotional intensity of seed videos by assessing them from a set of predefined emotion categories (i.e., "Admiration", "Amusement", "Determination", "Empathic Pain", "Excitement" and "Joy"). To tackle this challenge, we extracted rich dual-channel visual features based on ResNet18 and AUs for the video modality and effective single-channel features based on Wav2Vec2.0 for the audio modality. This allowed us to obtain comprehensive emotional features for the audiovisual modality. Additionally, leveraging a late fusion strategy, we averaged the predictions of the visual and acoustic models, resulting in a more accurate estimation of audiovisual emotional mimicry intensity. Experimental results validate the effectiveness of our approach, with the average Pearson's correlation Coefficient($ρ$) across the 6 emotion dimensionson the validation set achieving 0.3288.
Demystifying the DAO Governance Process
Venue: arXiv cs.SE 2024
Year: 2024

Decentralized Autonomous Organization (DAO) becomes a popular governance solution for decentralized applications (dApps) to achieve decentralized governance. In the DAO, no single entity can arbitrarily control the dApps without approval from the majority of members. However, despite its advantages, DAO has also been targeted by several attacks, leading to the loss of millions of dollars. In this paper, we first provided an overview of the DAO governance process within the blockchain. Next, we identified the issues within three components of governance process: Governance Contract, Documentation, and Proposal. Each of these components is vulnerable to issues that could potentially result in substantial financial losses. Then we developed automated methods to detected above issues. To investigate the issues within the existing DAO ecosystem, we constructed a state-of-the-art dataset that includes 16,427 DAOs, 183 documentation, and 122,307 proposals across 9 different blockchains. Our analysis reveals that a majority of DAO developers and members have not given sufficient attention to these issues, especially in the area of proposal. The result shows that over 60% of the examined proposals fail to provide a consistent description and code for their members, highlighting a significant gap in ensuring transparency within the DAO governance process. For a better DAO governance ecosystem, DAO developers and members can utilize the methods to identify and address issues within governance process.
Why E.T. Can't Phone Home: A Global View on IP-based Geoblocking at VoWiFi
Venue: arXiv cs.NI 2024
Year: 2024

In current cellular network generations (4G, 5G) the IMS (IP Multimedia Subsystem) plays an integral role in terminating voice calls and short messages. Many operators use VoWiFi (Voice over Wi-Fi, also Wi-Fi calling) as an alternative network access technology to complement their cellular coverage in areas where no radio signal is available (e.g., rural territories or shielded buildings). In a mobile world where customers regularly traverse national borders, this can be used to avoid expensive international roaming fees while journeying overseas, since VoWiFi calls are usually invoiced at domestic rates. To not lose this revenue stream, some operators block access to the IMS for customers staying abroad. This work evaluates the current deployment status of VoWiFi among worldwide operators and analyzes existing geoblocking measures on the IP layer by measuring connectivity from over 200 countries. We show that a substantial share (IPv4: 14.6%, IPv6: 65.2%) of operators implement geoblocking at the DNS- or VoWiFi protocol level, and highlight severe drawbacks in terms of emergency calling service availability.
Modality-Agnostic fMRI Decoding of Vision and Language
Venue: arXiv cs.CV 2024
Year: 2024

Previous studies have shown that it is possible to map brain activation data of subjects viewing images onto the feature representation space of not only vision models (modality-specific decoding) but also language models (cross-modal decoding). In this work, we introduce and use a new large-scale fMRI dataset (~8,500 trials per subject) of people watching both images and text descriptions of such images. This novel dataset enables the development of modality-agnostic decoders: a single decoder that can predict which stimulus a subject is seeing, irrespective of the modality (image or text) in which the stimulus is presented. We train and evaluate such decoders to map brain signals onto stimulus representations from a large range of publicly available vision, language and multimodal (vision+language) models. Our findings reveal that (1) modality-agnostic decoders perform as well as (and sometimes even better than) modality-specific decoders (2) modality-agnostic decoders mapping brain data onto representations from unimodal models perform as well as decoders relying on multimodal representations (3) while language and low-level visual (occipital) brain regions are best at decoding text and image stimuli, respectively, high-level visual (temporal) regions perform well on both stimulus types.
Locomotion Generation for a Rat Robot based on Environmental Changes via Reinforcement Learning
Venue: arXiv cs.RO 2024
Year: 2024

This research focuses on developing reinforcement learning approaches for the locomotion generation of small-size quadruped robots. The rat robot NeRmo is employed as the experimental platform. Due to the constrained volume, small-size quadruped robots typically possess fewer and weaker sensors, resulting in difficulty in accurately perceiving and responding to environmental changes. In this context, insufficient and imprecise feedback data from sensors makes it difficult to generate adaptive locomotion based on reinforcement learning. To overcome these challenges, this paper proposes a novel reinforcement learning approach that focuses on extracting effective perceptual information to enhance the environmental adaptability of small-size quadruped robots. According to the frequency of a robot's gait stride, key information of sensor data is analyzed utilizing sinusoidal functions derived from Fourier transform results. Additionally, a multifunctional reward mechanism is proposed to generate adaptive locomotion in different tasks. Extensive simulations are conducted to assess the effectiveness of the proposed reinforcement learning approach in generating rat robot locomotion in various environments. The experiment results illustrate the capability of the proposed approach to maintain stable locomotion of a rat robot across different terrains, including ramps, stairs, and spiral stairs.
Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling
Venue: arXiv cs.CV 2024
Year: 2024

Shape reconstruction from imaging volumes is a recurring need in medical image analysis. Common workflows start with a segmentation step, followed by careful post-processing and,finally, ad hoc meshing algorithms. As this sequence can be timeconsuming, neural networks are trained to reconstruct shapes through template deformation. These networks deliver state-ofthe-art results without manual intervention, but, so far, they have primarily been evaluated on anatomical shapes with little topological variety between individuals. In contrast, other works favor learning implicit shape models, which have multiple benefits for meshing and visualization. Our work follows this direction by introducing deep medial voxels, a semi-implicit representation that faithfully approximates the topological skeleton from imaging volumes and eventually leads to shape reconstruction via convolution surfaces. Our reconstruction technique shows potential for both visualization and computer simulations.
Unimodal Multi-Task Fusion for Emotional Mimicry Intensity Prediction
Venue: arXiv cs.SD 2024
Year: 2024

In this research, we introduce a novel methodology for assessing Emotional Mimicry Intensity (EMI) as part of the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild. Our methodology utilises the Wav2Vec 2.0 architecture, which has been pre-trained on an extensive podcast dataset, to capture a wide array of audio features that include both linguistic and paralinguistic components. We refine our feature extraction process by employing a fusion technique that combines individual features with a global mean vector, thereby embedding a broader contextual understanding into our analysis. A key aspect of our approach is the multi-task fusion strategy that not only leverages these features but also incorporates a pre-trained Valence-Arousal-Dominance (VAD) model. This integration is designed to refine emotion intensity prediction by concurrently processing multiple emotional dimensions, thereby embedding a richer contextual understanding into our framework. For the temporal analysis of audio data, our feature fusion process utilises a Long Short-Term Memory (LSTM) network. This approach, which relies solely on the provided audio data, shows marked advancements over the existing baseline, offering a more comprehensive understanding of emotional mimicry in naturalistic settings, achieving the second place in the EMI challenge.
SuperLoRA: Parameter-Efficient Unified Adaptation of Multi-Layer Attention Modules
Venue: arXiv cs.CV 2024
Year: 2024

Low-rank adaptation (LoRA) and its variants are widely employed in fine-tuning large models, including large language models for natural language processing and diffusion models for computer vision. This paper proposes a generalized framework called SuperLoRA that unifies and extends different LoRA variants, which can be realized under different hyper-parameter settings. Introducing grouping, folding, shuffling, projecting, and tensor factoring, SuperLoRA offers high flexibility compared with other LoRA variants and demonstrates superior performance for transfer learning tasks especially in the extremely few-parameter regimes.